Development of a Machine Learning Interatomic Potential for Uranium Nitride

Machine learning interatomic potentials (MLIPs) have become widely popular due to their high accuracy, similar to first-principles calculations, at a low computational cost. Recently, we have demonstrated that MLIPs can be very useful to investigate the atomic dynamics of actinide compounds used as nuclear fuels. In this study, we have extended previous work by developing an MLIP for uranium mononitride (UN). We employ an on-the-fly active learning sampling approach to iteratively improve our MLIP by generating an optimal and informative first-principles dataset. We utilize the MLIP to calculate important physical quantities, such as specific heat, bulk modulus, and relevant point defect energies with high accuracy in comparison to first-principles and experimental data.

LA-UR-23-31944